Serological evidence for Japanese encephalitis and West Nile virus infections in domestic birds in Cambodia

Mosquito-borne flaviviruses with an enzootic transmission cycle like Japanese encephalitis virus (JEV) and West Nile virus (WNV) are a major public health concern. The circulation of JEV in Southeast Asia is well-documented, and the important role of pigs as amplification hosts for the virus is long known. The influence of other domestic animals especially poultry that lives in high abundance and close proximity to humans is not intensively analyzed. Another understudied field in Asia is the presence of the closely related WNV. Such analyses are difficult to perform due to the intense antigenic cross-reactivity between these viruses and the lack of suitable standardized serological assays. The main objective of this study was to assess the prevalence of JEV and WNV flaviviruses in domestic birds, detailed in chickens and ducks, in three different Cambodian provinces. We determined the flavivirus seroprevalence using an hemagglutination inhibition assay (HIA). Additionally, we investigated in positive samples the presence of JEV and WNV neutralizing antibodies (nAb) using foci reduction neutralization test (FRNT). We found 29% (180/620) of the investigated birds positive for flavivirus antibodies with an age-depended increase of the seroprevalence (OR = 1.04) and a higher prevalence in ducks compared to chicken (OR = 3.01). Within the flavivirus-positive birds, we found 43% (28/65) with nAb against JEV. We also observed the expected cross-reactivity between JEV and WNV, by identifying 18.5% double-positive birds that had higher titers of nAb than single-positive birds. Additionally, seven domestic birds (10.7%) showed only nAb against WNV and no nAb against JEV. Our study provides evidence for an intense JEV circulation in domestic birds in Cambodia, and the first serological evidence for WNV presence in Southeast Asia since decades. These findings mark the need for a re-definition of areas at risk for JEV and WNV transmission, and the need for further and intensified surveillance of mosquito-transmitted diseases in domestic animals

Multi-species temporal network of livestock movements for disease spread

Introduction: The objective of this study is to show the importance of interspecies links and temporal network dynamics of a multi-species livestock movement network. Although both cattle and sheep networks have been previously studied, cattle-sheep multi-species networks have not generally been studied in-depth. The central question of this study is how the combination of cattle and sheep movements affects the potential for disease spread on the combined network. Materials and methods: Our analysis considers static and temporal representations of networks based on recorded animal movements. We computed network-based node importance measures of two single-species networks, and compared the top-ranked premises with the ones in the multi-species network. We propose the use of a measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. To ground our investigation in infectious disease epidemiology, we compared this suggested measure with the results of disease simulation models with asymmetric probabilities of transmission between species. Results: Our analysis of the temporal networks shows that the premises which are likely to drive the epidemic in this multi-species network differ from the ones in both the cattle and the sheep networks. Although sheep movements are highly seasonal, the estimated size of an epidemic is significantly larger in the multi-species network than in the cattle network, independently of the period of the year. Finally, we demonstrate that a measure based on contact chains allow us to identify around 30% of the key farms in a simulated epidemic, ignoring markets, whilst static network measures identify less than 10% of these farms. Conclusion: Our results ascertain the importance of combining species networks, as well as considering layers of temporal livestock movements in detail for the study of disease spread

Movement, mobility and disease modelling in three epidemic contexts

Infectious diseases have played a considerable role in shaping human history. Although their global burden has significantly decreased through the past centuries, they are still among the main causes of human death worldwide. In livestock, infectious diseases can cause substantial production losses but also have detrimental impacts upon human health, and animal health and welfare. Changes in practices and development of treatments and vaccines have helped to dramatically mitigate the impact of infectious diseases, but infectious diseases remain an ongoing challenge, either because they are difficult to control (Tuberculosis, Malaria, HIV, FMD) or because they are emerging or re-emerging pathogens. Human mobility and livestock movements play a crucial role in epidemic spread as they allow for long-range transmission and can act as bridge between otherwise disconnected populations. Repeated importations of cases in disease-free areas make the eradication or control of a disease exceedingly difficult. The patterns of potentially infectious contacts, as recorded in mobility and movement data, can be described as a network. Understanding infection transmission on networks can provide useful insights in disease risk. Mathematical models have played an increasingly important role in helping to control epidemics in animal (FMD, Avian Influenza, Swine Fever) and in human (Measle, Malaria, SARS, Ebola) populations. Modelling tools are now a central feature in the decision-making process for policy makers, as illustrated by the ongoing COVID-19 pandemic and its management. The aim of this work is to show how disease models in combination with movement or mobility data can be useful in different epidemic contexts, namely in peacetime, at the start of an outbreak and once the pathogen is circulating. Part One investigates how livestock movement data and network analysis can be used in peacetime to improve our understanding of disease risk and to propose tools for control. In this part, I consider a fast-spreading disease affecting cattle and sheep. First, I use multi-species movement networks to understand how the combination of cattle and sheep movement affects the potential for disease spread on the combined network. I compare results of single-species vs multi-species and static vs dynamic network analyses to show the importance of interspecies links and temporal network dynamics. My results show that depending on the season, up to 70% of the premises which are likely to drive the epidemic in the multi-species network differ from the ones in both the cattle and the sheep networks. This indicates that their risk is derived from interaction between the two farming systems. Secondly, I propose the use of a dynamic network measure based on contact chains calculated in a network weighted with transmission probabilities to assess the importance of premises in an outbreak. Comparing results with disease simulation model outputs, I demonstrate that the measure proposed allows us to identify around 30% of the key farms in a simulated epidemic, ignoring markets. Whereas static network measures identify less than 10% of these farms. Part Two explores how mobility data within disease models can be used during an epidemic: before the pathogen is introduced (importation phase) and once the pathogen is present (circulation phase). In this part, I use the COVID-19 pandemic and its spread in the Scottish Hebrides, an archipelago off the west coast of Scotland. First, human mobility data and a metapopulation model are used to estimate the risk of introduction in each of the Islands, according to season and potential for control. I show that in some islands the introduction risk is high even in the low season, when activity and movements from the mainland are expected to be reduced. This will be of particular concern if COVID-19 becomes a seasonal respiratory infection affecting temperate areas in winter concomitantly with other seasonal infections such as flu. In the high season, although in most cases movement control will not significantly delay a potential introduction, for some islands a 70% reduction of movements in peak summer tourist season has the potential for delaying the introduction risk for over 6 weeks, i.e. beyond the high risk summer holiday period. Secondly, data from an outbreak localised in Barra Island (Western Hebrides) are used to illustrate how adjusting model parameters to disease data can provide insight in transmission dynamic and control measure efficacy. Using Approximate Bayesian Inference, I estimate the most likely date of introduction, the basic reproduction number at the start of the outbreak and I quantify the impact of voluntary vs policy-induced measures. I find that transmission started to slow down two days after the first cases were reported and a week before restrictions were imposed by the authorities. Thus my analysis is most consistent with the outbreak being mostly contained by a combination of contact tracing and self-imposed measures, whilst the lockdown, which was later imposed, had only a negligible effect on the transmission dynamic

Spatial Multicriteria Evaluation for Mapping the Risk of Occurrence of Peste des Petits Ruminants in Eastern Africa and the Union of the Comoros

International audiencePeste des petits ruminants virus (PPRV), responsible for peste des petits ruminants (PPR), is widely circulating in Africa and Asia. The disease is a huge burden for the economy and development of the affected countries. In Eastern Africa, the disease is considered endemic. Because of the geographic proximity and existing trade between eastern African countries and the Comoros archipelago, the latter is at risk of introduction and spread, and the first PPR outbreaks occurred in the Union of the Comoros in 2012. The objective of this study was to map the areas suitable for PPR occurrence and spread in the Union of the Comoros and four eastern African countries, namely Ethiopia, Uganda, Kenya, and Tanzania. A Geographic Information System (GIS)-based Multicriteria Evaluation (MCE) was developed. Risk factors for PPR occurrence and spread, and their relative importance, were identified using literature review and expert-based knowledge. Corresponding geographic data were collected, standardized, and combined based on a weighted linear combination to obtain PPR suitability maps. The accuracy of the maps was assessed using outbreak data from the EMPRES database and a ROC curve analysis. Our model showed an excellent ability to distinguish between absence and presence of outbreaks in Eastern Africa (AUC = 0.907; 95% CI [0.820-0.994]), and a very good performance in the Union of the Comoros (AUC = 0.889, 95% CI: [0.694-1]). These results highlight the efficiency of the GIS-MCE method, which can be applied at different geographic scales: continental, national and local. The resulting maps provide decision support tools for implementation of disease surveillance and control measures, thus contributing to the PPR eradication goal of OIE and FAO by 2030

SCoVMod - a spatially explicit mobility and deprivation adjusted model of first wave COVID-19 transmission dynamics

Background: Mobility restrictions prevent the spread of infections to disease-free areas, and early in the coronavirus disease 2019 (COVID-19) pandemic, most countries imposed severe restrictions on mobility as soon as it was clear that containment of local outbreaks was insufficient to control spread. These restrictions have adverse impacts on the economy and other aspects of human health, and it is important to quantify their impact for evaluating their future value. Methods: Here we develop Scotland Coronavirus transmission Model (SCoVMod), a model for COVID-19 in Scotland, which presents unusual challenges because of its diverse geography and population conditions. Our fitted model captures spatio-temporal patterns of mortality in the first phase of the epidemic to a fine geographical scale. Results: We find that lockdown restrictions reduced transmission rates down to an estimated 12\% of its pre-lockdown rate. We show that, while the timing of COVID-19 restrictions influences the role of the transmission rate on the number of COVID-related deaths, early reduction in long distance movements does not. However, poor health associated with deprivation has a considerable association with mortality; the Council Area (CA) with the greatest health-related deprivation was found to have a mortality rate 2.45 times greater than the CA with the lowest health-related deprivation considering all deaths occurring outside of carehomes. Conclusions: We find that in even an early epidemic with poor case ascertainment, a useful spatially explicit model can be fit with meaningful parameters based on the spatio-temporal distribution of death counts. Our simple approach is useful to strategically examine trade-offs between travel related restrictions and physical distancing, and the effect of deprivation-related factors on outcomes

Transferts hydriques aquifère-sol-végétation-atmosphère

National audienc

Transferts hydriques aquifère-sol-végétation-atmosphère

National audienc